<?xml version="1.0"?>
<doc>
    <assembly>
        <name>AForge.Fuzzy</name>
    </assembly>
    <members>
        <member name="T:AForge.Fuzzy.Rulebase">
            <summary>
            The class represents a fuzzy rulebase, a set of fuzzy rules used in a Fuzzy Inference System.
            </summary>
            
        </member>
        <member name="M:AForge.Fuzzy.Rulebase.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.Rulebase"/> class.
            </summary>
            
        </member>
        <member name="M:AForge.Fuzzy.Rulebase.AddRule(AForge.Fuzzy.Rule)">
            <summary>
            Adds a fuzzy rule to the database. 
            </summary>
            
            <param name="rule">A fuzzy <see cref="T:AForge.Fuzzy.Rule"/> to add to the database.</param>
            
            <exception cref="T:System.NullReferenceException">The fuzzy rule was not initialized.</exception>
            <exception cref="T:System.ArgumentException">The fuzzy rule name already exists in the rulebase.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.Rulebase.ClearRules">
            <summary>
            Removes all the fuzzy rules of the database. 
            </summary>
            
        </member>
        <member name="M:AForge.Fuzzy.Rulebase.GetRule(System.String)">
            <summary>
            Returns an existing fuzzy rule from the rulebase.
            </summary>
            
            <param name="ruleName">Name of the fuzzy <see cref="T:AForge.Fuzzy.Rule"/> to retrieve.</param>
            
            <returns>Reference to named <see cref="T:AForge.Fuzzy.Rule"/>.</returns>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The rule indicated in ruleName was not found in the rulebase.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.Rulebase.GetRules">
            <summary>
            Gets all the rules of the rulebase.
            </summary>
            
            <returns>An array with all the rulebase rules.</returns>
            
        </member>
        <member name="T:AForge.Fuzzy.IDefuzzifier">
            <summary>
            Interface which specifies set of methods required to be implemented by all defuzzification methods 
            that can be used in Fuzzy Inference Systems. 
            </summary>
            
            <remarks><para>In many applications, a Fuzzy Inference System is used to perform linguistic computation, 
            but at the end of the inference process, a numerical value is needed. It does not mean that the system 
            needs precision, but simply that a numerical value is required, most of the times because it will be used to 
            control another system that needs the number. To obtain this numer, a defuzzification method is performed.</para>
            
            <para>Several deffuzification methods were proposed, and they can be created as classes that 
            implements this interface.</para></remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.IDefuzzifier.Defuzzify(AForge.Fuzzy.FuzzyOutput,AForge.Fuzzy.INorm)">
            <summary>
            Defuzzification method to obtain the numerical representation of a fuzzy output.
            </summary>
            
            <param name="fuzzyOutput">A <see cref="T:AForge.Fuzzy.FuzzyOutput"/> containing the output of
            several rules of a Fuzzy Inference System.</param>
            <param name="normOperator">A <see cref="T:AForge.Fuzzy.INorm"/> operator to be used when constraining
            the label to the firing strength.</param>
            
            <returns>The numerical representation of the fuzzy output.</returns>
            
        </member>
        <member name="T:AForge.Fuzzy.NotOperator">
            <summary>
            NOT operator, used to calculate the complement of a fuzzy set. 
            </summary>
            
            <remarks><para>The NOT operator definition is (1 - m) for all the values of membership m of the fuzzy set.</para>
            
            <para>Sample usage:</para>
            <code>
            // creating a fuzzy sets to represent Cool (Temperature)
            TrapezoidalFunction function1 = new TrapezoidalFunction( 13, 18, 23, 28 );
            FuzzySet fsCool = new FuzzySet( "Cool", function1 );
            
            // getting membership
            float m1 = fsCool.GetMembership( 15 );
            
            // computing the membership of "NOT Cool"
            NotOperator NOT = new NotOperator( );
            float result = NOT.Evaluate( m1 );
                         
            // show result
            Console.WriteLine( result );
            </code>
            </remarks>
            
            <seealso cref="T:AForge.Fuzzy.IUnaryOperator"/>
            
        </member>
        <member name="T:AForge.Fuzzy.IUnaryOperator">
            <summary>
            Interface with the common methods of Fuzzy Unary Operator.
            </summary>
            
            <remarks><para>All fuzzy operators that act as a Unary Operator (NOT, VERY, LITTLE) must implement this interface.
            </para></remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.IUnaryOperator.Evaluate(System.Single)">
            <summary>
            Calculates the numerical result of a Unary operation applied to one
            fuzzy membership value.
            </summary>
            
            <param name="membership">A fuzzy membership value, [0..1].</param>
            
            <returns>The numerical result of the operation applied to <paramref name="membership"/></returns>.
            
        </member>
        <member name="M:AForge.Fuzzy.NotOperator.Evaluate(System.Single)">
            <summary>
            Calculates the numerical result of the NOT operation applied to
            a fuzzy membership value.
            </summary>
            
            <param name="membership">A fuzzy membership value, [0..1].</param>
            
            <returns>The numerical result of the unary operation NOT applied to <paramref name="membership"/>.</returns>
            
        </member>
        <member name="T:AForge.Fuzzy.MaximumCoNorm">
            <summary>
            Maximum CoNorm, used to calculate the linguistic value of a OR operation. 
            </summary>
            
            <remarks><para>The maximum CoNorm uses a maximum operator to compute the OR
            among two fuzzy memberships.</para>
            
            <para>Sample usage:</para>
            <code>
            // creating 2 fuzzy sets to represent Cool (Temperature) and Near (Distance)
            TrapezoidalFunction function1 = new TrapezoidalFunction( 13, 18, 23, 28 );
            FuzzySet fsCool = new FuzzySet( "Cool", function1 );
            TrapezoidalFunction function2 = new TrapezoidalFunction( 23, 28, 33, 38 );
            FuzzySet fsNear = new FuzzySet( "Near", function2 );
            
            // getting memberships
            float m1 = fsCool.GetMembership( 15 );
            float m2 = fsNear.GetMembership( 35 );
            
            // computing the membership of "Cool OR Near"
            MaximumCoNorm OR = new MaximumCoNorm( );
            float result = OR.Evaluate( m1, m2 );
                         
            // show result
            Console.WriteLine( result );
            </code>
            </remarks>
            
            <seealso cref="T:AForge.Fuzzy.ICoNorm"/>
            
        </member>
        <member name="T:AForge.Fuzzy.ICoNorm">
            <summary>
            Interface with the common methods of a Fuzzy CoNorm.
            </summary>
            
            <remarks><para>All fuzzy operators that act as a CoNorm must implement this interface.
            </para></remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.ICoNorm.Evaluate(System.Single,System.Single)">
            <summary>
            Calculates the numerical result of a CoNorm (OR) operation applied to
            two fuzzy membership values.
            </summary>
            
            <param name="membershipA">A fuzzy membership value, [0..1].</param>
            <param name="membershipB">A fuzzy membership value, [0..1].</param>
            
            <returns>The numerical result the operation OR applied to <paramref name="membershipA"/>
            and <paramref name="membershipB"/>.</returns>
            
        </member>
        <member name="M:AForge.Fuzzy.MaximumCoNorm.Evaluate(System.Single,System.Single)">
            <summary>
            Calculates the numerical result of the OR operation applied to
            two fuzzy membership values.
            </summary>
            
            <param name="membershipA">A fuzzy membership value, [0..1].</param>
            <param name="membershipB">A fuzzy membership value, [0..1].</param>
            
            <returns>The numerical result of the binary operation OR applied to <paramref name="membershipA"/>
            and <paramref name="membershipB"/>.</returns>
            
        </member>
        <member name="T:AForge.Fuzzy.SingletonFunction">
            <summary>
            Membership function used in fuzzy singletons: fuzzy sets that have just one point with membership value 1. 
            </summary>
            
            <remarks><para>Sometimes it is needed to represent crisp (classical) number in the fuzzy domain. Several approaches 
            can be used, like adding some uncertain (fuzziness) in the original number (the number one, for instance, can be seen as a <see cref="T:AForge.Fuzzy.TrapezoidalFunction"/>
            with -0.5, 1.0 and 0.5 parameters). Another approach is to declare fuzzy singletons: fuzzy sets with only one point returning a none zero membership.</para>
            
            <para>While trapezoidal and half trapezoidal are classic functions used in fuzzy functions, this class supports any function
            or approximation that can be represented as a sequence of lines.</para>
            
            <para>Sample usage:</para>
            <code>
            // creating the instance
            SingletonFunction membershipFunction = new SingletonFunction( 10 );
            // getting membership for several points
            for ( int i = 0; i &lt; 20; i++ )
                Console.WriteLine( membershipFunction.GetMembership( i ) );
            </code>
            </remarks>
            
        </member>
        <member name="T:AForge.Fuzzy.IMembershipFunction">
            <summary>
            Interface which specifies set of methods required to be implemented by all membership
            functions.
            </summary>
            
            <remarks><para>All membership functions must implement this interface, which is used by
            <see cref="T:AForge.Fuzzy.FuzzySet"/> class to calculate value's membership to a particular fuzzy set.
            </para></remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.IMembershipFunction.GetMembership(System.Single)">
            <summary>
            Calculate membership of a given value to the fuzzy set.
            </summary>
            
            <param name="x">Value which membership will to be calculated.</param>
            
            <returns>Degree of membership [0..1] of the value to the fuzzy set.</returns>
            
        </member>
        <member name="P:AForge.Fuzzy.IMembershipFunction.LeftLimit">
            <summary>
            The leftmost x value of the membership function.
            </summary>
        </member>
        <member name="P:AForge.Fuzzy.IMembershipFunction.RightLimit">
            <summary>
            The rightmost x value of the membership function.
            </summary>
        </member>
        <member name="F:AForge.Fuzzy.SingletonFunction.support">
            <summary>
            The unique point where the membership value is 1.
            </summary>
        </member>
        <member name="M:AForge.Fuzzy.SingletonFunction.#ctor(System.Single)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.SingletonFunction"/> class. 
            </summary>
            
            <param name="support">Support is the only value of x where the membership function is 1.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.SingletonFunction.GetMembership(System.Single)">
            <summary>
            Calculate membership of a given value to the singleton function.
            </summary>
            
            <param name="x">Value which membership will to be calculated.</param>
            
            <returns>Degree of membership {0,1} since singletons do not admit memberships different from 0 and 1. </returns>
            
        </member>
        <member name="P:AForge.Fuzzy.SingletonFunction.LeftLimit">
            <summary>
            The leftmost x value of the membership function, the same value of the support.
            </summary>
            
        </member>
        <member name="P:AForge.Fuzzy.SingletonFunction.RightLimit">
            <summary>
            The rightmost x value of the membership function, the same value of the support.
            </summary>
            
        </member>
        <member name="T:AForge.Fuzzy.InferenceSystem">
            <summary>
            This class represents a Fuzzy Inference System. 
            </summary>
            
            <remarks><para>A Fuzzy Inference System is a model capable of executing fuzzy computing.
            It is mainly composed by a <see cref="T:AForge.Fuzzy.Database"/> with the linguistic variables
            (see <see cref="T:AForge.Fuzzy.LinguisticVariable"/>) and a <see cref="T:AForge.Fuzzy.Rulebase"/>
            with the fuzzy rules (see <see cref="T:AForge.Fuzzy.Rule"/>) that represent the behavior of the system.
            The typical operation of a Fuzzy Inference System is:
            <list type="bullet">
            <item>Get the numeric inputs;</item>
            <item>Use the <see cref="T:AForge.Fuzzy.Database"/> with the linguistic variables
            (see <see cref="T:AForge.Fuzzy.LinguisticVariable"/>) to obtain linguistic meaning for each
            numerical input;</item>
            <item>Verify which rules (see <see cref="T:AForge.Fuzzy.Rule"/>) of the <see cref="T:AForge.Fuzzy.Rulebase"/> are
            activated by the input;</item>
            <item>Combine the consequent of the activated rules to obtain a <see cref="T:AForge.Fuzzy.FuzzyOutput"/>;</item>
            <item>Use some defuzzifier (see <see cref="T:AForge.Fuzzy.IDefuzzifier"/>) to obtain a numerical output. </item>
            </list>
            </para>
            
            <para>The following sample usage is a Fuzzy Inference System that controls an
            auto guided vehicle avoing frontal collisions:</para>
            <code>
            // linguistic labels (fuzzy sets) that compose the distances
            FuzzySet fsNear = new FuzzySet( "Near",
                new TrapezoidalFunction( 15, 50, TrapezoidalFunction.EdgeType.Right ) );
            FuzzySet fsMedium = new FuzzySet( "Medium",
                new TrapezoidalFunction( 15, 50, 60, 100 ) );
            FuzzySet fsFar = new FuzzySet( "Far",
                new TrapezoidalFunction( 60, 100, TrapezoidalFunction.EdgeType.Left ) );
                        
            // front distance (input)
            LinguisticVariable lvFront = new LinguisticVariable( "FrontalDistance", 0, 120 );
            lvFront.AddLabel( fsNear );
            lvFront.AddLabel( fsMedium );
            lvFront.AddLabel( fsFar );
            
            // linguistic labels (fuzzy sets) that compose the angle
            FuzzySet fsZero = new FuzzySet( "Zero",
                new TrapezoidalFunction( -10, 5, 5, 10 ) );
            FuzzySet fsLP = new FuzzySet( "LittlePositive",
                new TrapezoidalFunction( 5, 10, 20, 25 ) );
            FuzzySet fsP = new FuzzySet( "Positive",
                new TrapezoidalFunction( 20, 25, 35, 40 ) );
            FuzzySet fsVP = new FuzzySet( "VeryPositive",
                new TrapezoidalFunction( 35, 40, TrapezoidalFunction.EdgeType.Left ) );
            
            // angle
            LinguisticVariable lvAngle = new LinguisticVariable( "Angle", -10, 50 );
            lvAngle.AddLabel( fsZero );
            lvAngle.AddLabel( fsLP );
            lvAngle.AddLabel( fsP );
            lvAngle.AddLabel( fsVP );
            
            // the database
            Database fuzzyDB = new Database( );
            fuzzyDB.AddVariable( lvFront );
            fuzzyDB.AddVariable( lvAngle );
            
            // creating the inference system
            InferenceSystem IS = new InferenceSystem( fuzzyDB, new CentroidDefuzzifier( 1000 ) );
            
            // going Straight
            IS.NewRule( "Rule 1", "IF FrontalDistance IS Far THEN Angle IS Zero" );
            // Turning Left
            IS.NewRule( "Rule 2", "IF FrontalDistance IS Near THEN Angle IS Positive" );
            
            ...
            // inference section
            
            // setting inputs
            IS.SetInput( "FrontalDistance", 20 );
            
            // getting outputs
            try
            {
                float newAngle = IS.Evaluate( "Angle" );
            }
            catch ( Exception )
            {
            ...
            }
            </code>    
            </remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.InferenceSystem.#ctor(AForge.Fuzzy.Database,AForge.Fuzzy.IDefuzzifier)">
            <summary>
            Initializes a new Fuzzy <see cref="T:AForge.Fuzzy.InferenceSystem"/>.
            </summary>
            
            <param name="database">A fuzzy <see cref="T:AForge.Fuzzy.Database"/> containing the system linguistic variables.</param>
            <param name="defuzzifier">A defuzzyfier method used to evaluate the numeric uotput of the system.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.InferenceSystem.#ctor(AForge.Fuzzy.Database,AForge.Fuzzy.IDefuzzifier,AForge.Fuzzy.INorm,AForge.Fuzzy.ICoNorm)">
            <summary>
            Initializes a new Fuzzy <see cref="T:AForge.Fuzzy.InferenceSystem"/>.
            </summary>
            
            <param name="database">A fuzzy <see cref="T:AForge.Fuzzy.Database"/> containing the system linguistic
            variables.</param>
            <param name="defuzzifier">A defuzzyfier method used to evaluate the numeric otput
            of the system.</param>
            <param name="normOperator">A <see cref="T:AForge.Fuzzy.INorm"/> operator used to evaluate the norms
            in the <see cref="T:AForge.Fuzzy.InferenceSystem"/>. For more information of the norm evaluation see <see cref="T:AForge.Fuzzy.Rule"/>.</param>
            <param name="conormOperator">A <see cref="T:AForge.Fuzzy.ICoNorm"/> operator used to evaluate the
            conorms in the <see cref="T:AForge.Fuzzy.InferenceSystem"/>. For more information of the conorm evaluation see <see cref="T:AForge.Fuzzy.Rule"/>.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.InferenceSystem.NewRule(System.String,System.String)">
            <summary>
            Creates a new <see cref="T:AForge.Fuzzy.Rule"/> and add it to the <see cref="T:AForge.Fuzzy.Rulebase"/> of the 
            <see cref="T:AForge.Fuzzy.InferenceSystem"/>.
            </summary>
            
            <param name="name">Name of the <see cref="T:AForge.Fuzzy.Rule"/> to create.</param>
            <param name="rule">A string representing the fuzzy rule.</param>
            
            <returns>The new <see cref="T:AForge.Fuzzy.Rule"/> reference. </returns>
            
        </member>
        <member name="M:AForge.Fuzzy.InferenceSystem.SetInput(System.String,System.Single)">
            <summary>
            Sets a numerical input for one of the linguistic variables of the <see cref="T:AForge.Fuzzy.Database"/>. 
            </summary>
            
            <param name="variableName">Name of the <see cref="T:AForge.Fuzzy.LinguisticVariable"/>.</param>
            <param name="value">Numeric value to be used as input.</param>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The variable indicated in <paramref name="variableName"/>
            was not found in the database.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.InferenceSystem.GetLinguisticVariable(System.String)">
            <summary>
            Gets one of the <see cref="T:AForge.Fuzzy.LinguisticVariable"/> of the <see cref="T:AForge.Fuzzy.Database"/>. 
            </summary>
            
            <param name="variableName">Name of the <see cref="T:AForge.Fuzzy.LinguisticVariable"/> to get.</param>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The variable indicated in <paramref name="variableName"/>
            was not found in the database.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.InferenceSystem.GetRule(System.String)">
            <summary>
            Gets one of the Rules of the <see cref="T:AForge.Fuzzy.Rulebase"/>. 
            </summary>
            
            <param name="ruleName">Name of the <see cref="T:AForge.Fuzzy.Rule"/> to get.</param>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The rule indicated in <paramref name="ruleName"/>
            was not found in the rulebase.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.InferenceSystem.Evaluate(System.String)">
            <summary>
            Executes the fuzzy inference, obtaining a numerical output for a choosen output
            linguistic variable. 
            </summary>
            
            <param name="variableName">Name of the <see cref="T:AForge.Fuzzy.LinguisticVariable"/> to evaluate.</param>
            
            <returns>The numerical output of the Fuzzy Inference System for the choosen variable.</returns>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The variable indicated was not found in the database.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.InferenceSystem.ExecuteInference(System.String)">
            <summary>
            Executes the fuzzy inference, obtaining the <see cref="T:AForge.Fuzzy.FuzzyOutput"/> of the system for the required
            <see cref="T:AForge.Fuzzy.LinguisticVariable"/>. 
            </summary>
            
            <param name="variableName">Name of the <see cref="T:AForge.Fuzzy.LinguisticVariable"/> to evaluate.</param>
            
            <returns>A <see cref="T:AForge.Fuzzy.FuzzyOutput"/> containing the fuzzy output of the system for the
            <see cref="T:AForge.Fuzzy.LinguisticVariable"/> specified in <paramref name="variableName"/>.</returns>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The variable indicated was not found in the database.</exception>
            
        </member>
        <member name="T:AForge.Fuzzy.CentroidDefuzzifier">
            <summary>
            This class implements the centroid defuzzification method.
            </summary>
            
            <remarks><para>In many applications, a Fuzzy Inference System is used to perform linguistic
            computation, but at the end of the inference process, a numerical value is needed. It does
            not mean that the system needs precision, but simply that a numerical value is required,
            most of the times because it will be used to control another system that needs the number.
            To obtain this numer, a defuzzification method is performed.</para>
            
            <para>This class implements the centroid defuzzification method. The output of a Fuzzy
            Inference System is a set of rules (see <see cref="T:AForge.Fuzzy.Rule"/>) with firing strength greater
            than zero. Those firing strength apply a constraint to the consequent fuzzy sets
            (see <see cref="T:AForge.Fuzzy.FuzzySet"/>) of the rules. Putting all those fuzzy sets togheter results
            in a a shape that is the linguistic output meaning. 
            </para>
            
            <para>The centroid method calculates the center of the area of this shape to obtain the
            numerical representation of the output. It uses a numerical approximation, so a number
            of intervals must be choosen. As the number of intervals grow, the precision of the
            numerical ouput grows. 
            </para>
            
            <para>For a sample usage of the <see cref="T:AForge.Fuzzy.CentroidDefuzzifier"/> see <see cref="T:AForge.Fuzzy.InferenceSystem"/>
            class.</para>
            </remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.CentroidDefuzzifier.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.CentroidDefuzzifier"/> class.
            </summary>
            
            <param name="intervals">Number of segments that the speech universe will be splited
            to perform the numerical approximation of the center of area.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.CentroidDefuzzifier.Defuzzify(AForge.Fuzzy.FuzzyOutput,AForge.Fuzzy.INorm)">
            <summary>
            Centroid method to obtain the numerical representation of a fuzzy output. The numerical
            value will be the center of the shape formed by the several fuzzy labels with their
            constraints.
            </summary>
            
            <param name="fuzzyOutput">A <see cref="T:AForge.Fuzzy.FuzzyOutput"/> containing the output of several
            rules of a Fuzzy Inference System.</param>
            <param name="normOperator">A <see cref="T:AForge.Fuzzy.INorm"/> operator to be used when constraining
            the label to the firing strength.</param>
            
            <returns>The numerical representation of the fuzzy output.</returns>
            
            <exception cref="T:System.Exception">The numerical output is unavaliable. All memberships are zero.</exception>
            
        </member>
        <member name="T:AForge.Fuzzy.ProductNorm">
            <summary>
            Product Norm, used to calculate the linguistic value of a AND operation. 
            </summary>
            
            <remarks><para>The product Norm uses a multiplication operator to compute the
            AND among two fuzzy memberships.</para>
            
            <para>Sample usage:</para>
            <code>
            // creating 2 fuzzy sets to represent Cool (Temperature) and Near (Distance)
            TrapezoidalFunction function1 = new TrapezoidalFunction( 13, 18, 23, 28 );
            FuzzySet fsCool = new FuzzySet( "Cool", function1 );
            TrapezoidalFunction function2 = new TrapezoidalFunction( 23, 28, 33, 38 );
            FuzzySet fsNear = new FuzzySet( "Near", function2 );
            
            // getting memberships
            float m1 = fsCool.GetMembership( 15 );
            float m2 = fsNear.GetMembership( 35 );
            
            // computing the membership of "Cool AND Near"
            ProductNorm AND = new ProductNorm( );
            float result = AND.Evaluate( m1, m2 );
                         
            // show result
            Console.WriteLine( result );
            </code>
            </remarks>
            
            <seealso cref="T:AForge.Fuzzy.MinimumNorm"/>
            
        </member>
        <member name="T:AForge.Fuzzy.INorm">
            <summary>
            Interface with the common methods of a Fuzzy Norm.
            </summary>
            
            <remarks><para>All fuzzy operators that act as a Norm must implement this interface.
            </para></remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.INorm.Evaluate(System.Single,System.Single)">
            <summary>
            Calculates the numerical result of a Norm (AND) operation applied to
            two fuzzy membership values.
            </summary>
            
            <param name="membershipA">A fuzzy membership value, [0..1].</param>
            <param name="membershipB">A fuzzy membership value, [0..1].</param>
            
            <returns>The numerical result the operation AND applied to <paramref name="membershipA"/>
            and <paramref name="membershipB"/>.</returns>
            
        </member>
        <member name="M:AForge.Fuzzy.ProductNorm.Evaluate(System.Single,System.Single)">
            <summary>
            Calculates the numerical result of the AND operation applied to
            two fuzzy membership values using the product rule.
            </summary>
            
            <param name="membershipA">A fuzzy membership value, [0..1].</param>
            <param name="membershipB">A fuzzy membership value, [0..1].</param>
            
            <returns>The numerical result of the AND operation applied to <paramref name="membershipA"/>
            and <paramref name="membershipB"/>.</returns>
            
        </member>
        <member name="T:AForge.Fuzzy.TrapezoidalFunction">
            <summary>
            Membership function in the shape of a trapezoid. Can be a half trapzoid if the left or the right side is missing. 
            </summary>
            
            <remarks><para>Since the <see cref="T:AForge.Fuzzy.PiecewiseLinearFunction"/> can represent any piece wise linear
            function, it can represent trapezoids too. But as trapezoids are largely used in the creation of
            Linguistic Variables, this class simplifies the creation of them. </para>
            
            <para>Sample usage:</para>
            <code>
            // creating a typical triangular fuzzy set /\
            TrapezoidalFunction function1 = new TrapezoidalFunction( 10, 20, 30 );
            // creating a right fuzzy set, the rigth side of the set is fuzzy but the left is opened
            TrapezoidalFunction function2 = new TrapezoidalFunction( 10, 20, 30, TrapezoidalFunction.EdgeType.Right );
            </code>
            
            </remarks>
        </member>
        <member name="T:AForge.Fuzzy.PiecewiseLinearFunction">
            <summary>
            Membership function composed by several connected linear functions.
            </summary>
            
            <remarks><para>The piecewise linear is a generic function used by many specific fuzzy membership 
            functions, like the <see cref="T:AForge.Fuzzy.TrapezoidalFunction">trappezoidal function</see>. This class must
            be instantiated with a sequence of points representing the edges of each one of the lines composing the 
            piecewise function.</para>
            
            <para><note>The x-axis points must be ordered (crescent), so the <see cref="M:AForge.Fuzzy.PiecewiseLinearFunction.GetMembership(System.Single)"/> function will use each X value 
            as an ending point for one line and starting point of the next.</note></para>
            
            <para>While trapezoidal and half trapezoidal are classic functions used in fuzzy functions, this class supports any function
            or approximation that can be represented as a sequence of lines.</para>
            
            <para>Sample usage:</para>
            <code>
            // creating an array of points representing a typical trapezoidal function /-\
            Point [] points = new Point[4];
            // point where membership starts to rise
            points[0] = new Point( 10, 0 );
            // maximum membership (1) reached at the second point 
            points[1] = new Point( 20, 1 );
            // membership starts to fall at the third point 
            points[2] = new Point( 30, 1 );
            // membership gets to zero at the last point 
            points[3] = new Point( 40, 0 );
            // creating the instance
            PiecewiseLinearFunction membershipFunction = new PiecewiseLinearFunction( points );
            // getting membership for several points
            for ( int i = 5; i &lt; 45; i++ )
                Console.WriteLine( membershipFunction.GetMembership( i ) );
            </code>
            </remarks>
            
        </member>
        <member name="F:AForge.Fuzzy.PiecewiseLinearFunction.points">
            <summary>
            Vector of (X,Y) coordinates for end/start of each line.
            </summary>
        </member>
        <member name="M:AForge.Fuzzy.PiecewiseLinearFunction.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.PiecewiseLinearFunction"/> class. 
            </summary>
            
            <remarks><para>This constructor must be used only by inherited classes to create the
            points vector after the instantiation.</para></remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.PiecewiseLinearFunction.#ctor(AForge.Point[])">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.PiecewiseLinearFunction"/> class.
            </summary>
            
            <param name="points">Array of (X,Y) coordinates of each start/end of the lines.</param>
            
            <remarks><para>Specified point must be in crescent order on X axis and their Y value
            must be in the range of [0, 1].</para></remarks>
            
            <exception cref="T:System.ArgumentException">Points must be in crescent order on X axis.</exception>
            <exception cref="T:System.ArgumentException">Y value of points must be in the range of [0, 1].</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.PiecewiseLinearFunction.GetMembership(System.Single)">
             <summary>
             Calculate membership of a given value to the piecewise function.
             </summary>
             
             <param name="x">Value which membership will to be calculated.</param>
             
             <returns>Degree of membership [0..1] of the value to the fuzzy set.</returns>
             
             <exception cref="T:System.NullReferenceException">Points of the membership function are not initialized.</exception>
            
        </member>
        <member name="P:AForge.Fuzzy.PiecewiseLinearFunction.LeftLimit">
             <summary>
             The leftmost x value of the membership function, given by the first (X,Y) coordinate.
             </summary>
             
             <exception cref="T:System.NullReferenceException">Points of the membership function are not initialized.</exception>
            
        </member>
        <member name="P:AForge.Fuzzy.PiecewiseLinearFunction.RightLimit">
             <summary>
             The rightmost x value of the membership function, given by the last (X,Y) coordinate.
             </summary>
             
             <exception cref="T:System.NullReferenceException">Points of the membership function are not initialized.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.TrapezoidalFunction.#ctor(System.Int32)">
            <summary>
            A private constructor used only to reuse code inside of this default constructor.
            </summary>
            
            <param name="size">Size of points vector to create. This size depends of the shape of the 
            trapezoid.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.TrapezoidalFunction.#ctor(System.Single,System.Single,System.Single,System.Single,System.Single,System.Single)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.TrapezoidalFunction"/> class.
            
            With four points the shape is known as flat fuzzy number or fuzzy interval (/--\).
            </summary>
            
            <param name="m1">X value where the degree of membership starts to raise.</param>
            <param name="m2">X value where the degree of membership reaches the maximum value.</param>
            <param name="m3">X value where the degree of membership starts to fall.</param>
            <param name="m4">X value where the degree of membership reaches the minimum value.</param>
            <param name="max">The maximum value that the membership will reach, [0, 1].</param>
            <param name="min">The minimum value that the membership will reach, [0, 1].</param>
            
        </member>
        <member name="M:AForge.Fuzzy.TrapezoidalFunction.#ctor(System.Single,System.Single,System.Single,System.Single)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.TrapezoidalFunction"/> class.
            
            With four points the shape is known as flat fuzzy number or fuzzy interval (/--\).
            </summary>
            
            <param name="m1">X value where the degree of membership starts to raise.</param>
            <param name="m2">X value where the degree of membership reaches the maximum value.</param>
            <param name="m3">X value where the degree of membership starts to fall.</param>
            <param name="m4">X value where the degree of membership reaches the minimum value.</param>
            
            <remarks>
            <para>Maximum membership value is set to <b>1.0</b> and the minimum is set to <b>0.0</b>.</para>
            </remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.TrapezoidalFunction.#ctor(System.Single,System.Single,System.Single,System.Single,System.Single)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.TrapezoidalFunction"/> class.
            
            With three points the shape is known as triangular fuzzy number or just fuzzy number (/\).
            </summary>
            
            <param name="m1">X value where the degree of membership starts to raise.</param>
            <param name="m2">X value where the degree of membership reaches the maximum value and starts to fall.</param>
            <param name="m3">X value where the degree of membership reaches the minimum value.</param>
            <param name="max">The maximum value that the membership will reach, [0, 1].</param>
            <param name="min">The minimum value that the membership will reach, [0, 1].</param>
            
        </member>
        <member name="M:AForge.Fuzzy.TrapezoidalFunction.#ctor(System.Single,System.Single,System.Single)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.TrapezoidalFunction"/> class.
            
            With three points the shape is known as triangular fuzzy number or just fuzzy number (/\).
            </summary>
            
            <param name="m1">X value where the degree of membership starts to raise.</param>
            <param name="m2">X value where the degree of membership reaches the maximum value and starts to fall.</param>
            <param name="m3">X value where the degree of membership reaches the minimum value.</param>
            
            <remarks>
            <para>Maximum membership value is set to <b>1.0</b> and the minimum is set to <b>0.0</b>.</para>
            </remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.TrapezoidalFunction.#ctor(System.Single,System.Single,System.Single,System.Single,AForge.Fuzzy.TrapezoidalFunction.EdgeType)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.TrapezoidalFunction"/> class.
            
            With two points and an edge this shape can be a left fuzzy number (/) or a right fuzzy number (\).
            </summary>
            
            <param name="m1">Edge = Left: X value where the degree of membership starts to raise.
            Edge = Right: X value where the function starts, with maximum degree of membership. </param>
            <param name="m2">Edge = Left: X value where the degree of membership reaches the maximum.
            Edge = Right: X value where the degree of membership reaches minimum value. </param>
            <param name="max">The maximum value that the membership will reach, [0, 1].</param>
            <param name="min">The minimum value that the membership will reach, [0, 1].</param>
            <param name="edge">Trapezoid's <see cref="T:AForge.Fuzzy.TrapezoidalFunction.EdgeType"/>.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.TrapezoidalFunction.#ctor(System.Single,System.Single,AForge.Fuzzy.TrapezoidalFunction.EdgeType)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.TrapezoidalFunction"/> class.
            
            With three points and an edge this shape can be a left fuzzy number (/--) or a right fuzzy number (--\).
            </summary>
            
            <param name="m1">Edge = Left: X value where the degree of membership starts to raise.
            Edge = Right: X value where the function starts, with maximum degree of membership. </param>
            <param name="m2">Edge = Left: X value where the degree of membership reaches the maximum.
            Edge = Right: X value where the degree of membership reaches minimum value. </param>
            <param name="edge">Trapezoid's <see cref="T:AForge.Fuzzy.TrapezoidalFunction.EdgeType"/>.</param>
            
            <remarks>
            <para>Maximum membership value is set to <b>1.0</b> and the minimum is set to <b>0.0</b>.</para>
            </remarks>
            
        </member>
        <member name="T:AForge.Fuzzy.TrapezoidalFunction.EdgeType">
            <summary>
            Enumeration used to create trapezoidal membership functions with half trapezoids.
            </summary>
            
            <remarks><para>If the value is Left, the trapezoid has the left edge, but right
            is open (/--). If the value is Right, the trapezoid has the right edge, but left
            is open (--\).</para></remarks>
            
        </member>
        <member name="F:AForge.Fuzzy.TrapezoidalFunction.EdgeType.Left">
            <summary>
            The fuzzy side of the trapezoid is at the left side.
            </summary>
        </member>
        <member name="F:AForge.Fuzzy.TrapezoidalFunction.EdgeType.Right">
            <summary>
            The fuzzy side of the trapezoid is at the right side.
            </summary>
        </member>
        <member name="T:AForge.Fuzzy.Database">
            <summary>
            The class represents a fuzzy database, a set of linguistic variables used in a Fuzzy
            Inference System.
            </summary>
            
        </member>
        <member name="M:AForge.Fuzzy.Database.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.Database"/> class.
            </summary>
            
        </member>
        <member name="M:AForge.Fuzzy.Database.AddVariable(AForge.Fuzzy.LinguisticVariable)">
            <summary>
            Adds a linguistic variable to the database. 
            </summary>
            
            <param name="variable">A linguistic variable to add.</param>
            
            <exception cref="T:System.NullReferenceException">The linguistic variable was not initialized.</exception>
            <exception cref="T:System.ArgumentException">The linguistic variable name already exists in the database.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.Database.ClearVariables">
            <summary>
            Removes all the linguistic variables of the database. 
            </summary>
            
        </member>
        <member name="M:AForge.Fuzzy.Database.GetVariable(System.String)">
            <summary>
            Returns an existing linguistic variable from the database.
            </summary>
            
            <param name="variableName">Name of the linguistic variable to retrieve.</param>
            
            <returns>Reference to named <see cref="T:AForge.Fuzzy.LinguisticVariable"/>.</returns>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The variable indicated was not found in the database.</exception>
            
        </member>
        <member name="T:AForge.Fuzzy.LinguisticVariable">
             <summary>
             The class represents a linguistic variable.
             </summary>
             
             <remarks><para>Linguistic variables are variables that store linguistic values (labels). Fuzzy Inference Systems (FIS)
             use a set of linguistic variables, called the FIS database, to execute fuzzy computation (computing with words). A linguistic
             variable has a name and is composed by a set of <see cref="T:AForge.Fuzzy.FuzzySet"/> called its linguistic labels. When declaring fuzzy 
             statements in a FIS, a linguistic variable can be only assigned or compared to one of its labels. </para>
             
             <para>Let us consider, for example, a linguistic variable <b>temperature</b>. In a given application, temperature can be 
             cold, cool, warm or hot. Those will be the variable's linguistic labels, each one a fuzzy set with its own membership 
             function. Ideally, the labels will represent concepts related to the variable's meaning. Futhermore, fuzzy statements like
             "temperature is warm" or "temperature is not cold" can be used to build a Fuzzy Inference Systems. 
             </para>
             
             <para>Sample usage:</para>
             <code>
             // create a linguistic variable to represent temperature
             LinguisticVariable lvTemperature = new LinguisticVariable( "Temperature", 0, 80 );
            
             // create the linguistic labels (fuzzy sets) that compose the temperature 
             TrapezoidalFunction function1 = new TrapezoidalFunction( 10, 15, TrapezoidalFunction.EdgeType.Right );
             FuzzySet fsCold = new FuzzySet( "Cold", function1 );
             TrapezoidalFunction function2 = new TrapezoidalFunction( 10, 15, 20, 25 );
             FuzzySet fsCool = new FuzzySet( "Cool", function2 );
             TrapezoidalFunction function3 = new TrapezoidalFunction( 20, 25, 30, 35 );
             FuzzySet fsWarm = new FuzzySet( "Warm", function3 );
             TrapezoidalFunction function4 = new TrapezoidalFunction( 30, 35, TrapezoidalFunction.EdgeType.Left );
             FuzzySet fsHot  = new FuzzySet( "Hot" , function4 );
            
             // adding labels to the variable
             lvTemperature.AddLabel( fsCold );
             lvTemperature.AddLabel( fsCool );
             lvTemperature.AddLabel( fsWarm );
             lvTemperature.AddLabel( fsHot  );
            
             // showing the shape of the linguistic variable - the shape of its labels memberships from start to end
             Console.WriteLine( "Cold; Cool; Warm; Hot" );
             for ( float x = 0; x &lt; 80; x += 0.2 )
             {
                 float y1 = lvTemperature.GetLabelMembership( "Cold", x );
                 float y2 = lvTemperature.GetLabelMembership( "Cool", x );
                 float y3 = lvTemperature.GetLabelMembership( "Warm", x );
                 float y4 = lvTemperature.GetLabelMembership( "Hot" , x );
            
                 Console.WriteLine( String.Format( "{0:N}; {1:N}; {2:N}; {3:N}", y1, y2, y3, y4 ) );
             }
             </code>    
             </remarks>
             
        </member>
        <member name="M:AForge.Fuzzy.LinguisticVariable.#ctor(System.String,System.Single,System.Single)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.LinguisticVariable"/> class.
            </summary>
            
            <param name="name">Name of the linguistic variable.</param>
            
            <param name="start">Left limit of the valid variable range.</param>
            
            <param name="end">Right limit of the valid variable range.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.LinguisticVariable.AddLabel(AForge.Fuzzy.FuzzySet)">
            <summary>
            Adds a linguistic label to the variable. 
            </summary>
            
            <param name="label">A <see cref="T:AForge.Fuzzy.FuzzySet"/> that will be a linguistic label of the linguistic variable.</param>
            
            <remarks>Linguistic labels are fuzzy sets (<see cref="T:AForge.Fuzzy.FuzzySet"/>). Each
            label of the variable must have a unique name. The range of the label 
            (left and right limits) cannot be greater than 
            the linguistic variable range (start/end).</remarks>
            
            <exception cref="T:System.NullReferenceException">The fuzzy set was not initialized.</exception>
            <exception cref="T:System.ArgumentException">The linguistic label name already exists in the linguistic variable.</exception>
            <exception cref="T:System.ArgumentException">The left limit of the fuzzy set can not be lower than the linguistic variable's starting point.</exception>
            <exception cref="T:System.ArgumentException">"The right limit of the fuzzy set can not be greater than the linguistic variable's ending point."</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.LinguisticVariable.ClearLabels">
            <summary>
            Removes all the linguistic labels of the linguistic variable. 
            </summary>
            
        </member>
        <member name="M:AForge.Fuzzy.LinguisticVariable.GetLabel(System.String)">
            <summary>
            Returns an existing label from the linguistic variable.
            </summary>
            
            <param name="labelName">Name of the label to retrieve.</param>
            
            <returns>Reference to named label (<see cref="T:AForge.Fuzzy.FuzzySet"/>).</returns>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The label indicated was not found in the linguistic variable.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.LinguisticVariable.GetLabelMembership(System.String,System.Single)">
            <summary>
            Calculate the membership of a given value to a given label. Used to evaluate linguistics clauses like 
            "X IS A", where X is a value and A is a linguistic label.
            </summary>
            
            <param name="labelName">Label (fuzzy set) to evaluate value's membership.</param>
            <param name="value">Value which label's membership will to be calculated.</param>
            
            <returns>Degree of membership [0..1] of the value to the label (fuzzy set).</returns>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The label indicated in labelName was not found in the linguistic variable.</exception>
            
        </member>
        <member name="P:AForge.Fuzzy.LinguisticVariable.NumericInput">
            <summary>
            Numerical value of the input of this linguistic variable.
            </summary>
        </member>
        <member name="P:AForge.Fuzzy.LinguisticVariable.Name">
            <summary>
            Name of the linguistic variable.
            </summary>
        </member>
        <member name="P:AForge.Fuzzy.LinguisticVariable.Start">
            <summary>
            Left limit of the valid variable range.
            </summary>
        </member>
        <member name="P:AForge.Fuzzy.LinguisticVariable.End">
            <summary>
            Right limit of the valid variable range.
            </summary>
        </member>
        <member name="T:AForge.Fuzzy.Rule">
             <summary>
             This class represents a Fuzzy Rule, a linguistic expression representing some behavioral
             aspect of a Fuzzy Inference System. 
             </summary>
             
             <remarks><para>
             A Fuzzy Rule is a fuzzy linguistic instruction that can be executed by a fuzzy system.
             The format of the Fuzzy Rule is:
             </para>
             
             <para><b>IF <i>antecedent</i> THEN <i>consequent</i></b></para>
             
             <para>The antecedent is composed by a set of fuzzy clauses (see <see cref="T:AForge.Fuzzy.Clause"/>) connected
             by fuzzy operations, like <b>AND</b> or <b>OR</b>. The operator <b>NOT</b> can be used to negate expressions: </para>
             
             <para><b>...<i>Clause1</i> AND (<i>Clause2</i> OR <i>Clause3</i>) AND NOT <i>Clause4</i> ...</b></para>
                 
             <para>Fuzzy clauses are written in form <i>Variable IS Value</i>. The NOT operator can be used to negate linguistic values as well:<br/>
             <b>...<i>Variable1 IS Value1</i> AND <i>Variable2 IS NOT Value2</i> ...</b></para>
            
             <para>The consequent is a single of fuzzy clauses (<see cref="T:AForge.Fuzzy.Clause"/>). To perform the
             linguistic computing, the <see cref="T:AForge.Fuzzy.Rule"/> evaluates the clauses and then applies the fuzzy
             operators. Once this is done a value representing the confidence in the antecedent being
             true is obtained, and this is called firing strength of the <see cref="T:AForge.Fuzzy.Rule"/>.</para>
             
             <para>The firing strength is used to discover with how much confidence the consequent
             of a rule is true.</para>
             
             <para>Sample usage:</para>
             <code>
             // create the linguistic labels (fuzzy sets) that compose the temperature 
             TrapezoidalFunction function1 = new TrapezoidalFunction(
                 10, 15, TrapezoidalFunction.EdgeType.Right );
             FuzzySet fsCold = new FuzzySet( "Cold", function1 );
             TrapezoidalFunction function2 = new TrapezoidalFunction( 10, 15, 20, 25 );
             FuzzySet fsCool = new FuzzySet( "Cool", function2 );
             TrapezoidalFunction function3 = new TrapezoidalFunction( 20, 25, 30, 35 );
             FuzzySet fsWarm = new FuzzySet( "Warm", function3 );
             TrapezoidalFunction function4 = new TrapezoidalFunction(
                 30, 35, TrapezoidalFunction.EdgeType.Left );
             FuzzySet fsHot = new FuzzySet( "Hot", function4 );
             
             // create a linguistic variable to represent steel temperature
             LinguisticVariable lvSteel = new LinguisticVariable( "Steel", 0, 80 );
             // adding labels to the variable
             lvSteel.AddLabel( fsCold );
             lvSteel.AddLabel( fsCool );
             lvSteel.AddLabel( fsWarm );
             lvSteel.AddLabel( fsHot );
             
             // create a linguistic variable to represent stove temperature
             LinguisticVariable lvStove = new LinguisticVariable( "Stove", 0, 80 );
             // adding labels to the variable
             lvStove.AddLabel( fsCold );
             lvStove.AddLabel( fsCool );
             lvStove.AddLabel( fsWarm );
             lvStove.AddLabel( fsHot );
             
             // create the linguistic labels (fuzzy sets) that compose the pressure
             TrapezoidalFunction function5 = new TrapezoidalFunction(
                 20, 40, TrapezoidalFunction.EdgeType.Right );
             FuzzySet fsLow = new FuzzySet( "Low", function5 );
             TrapezoidalFunction function6 = new TrapezoidalFunction( 20, 40, 60, 80 );
             FuzzySet fsMedium = new FuzzySet( "Medium", function6 );
             TrapezoidalFunction function7 = new TrapezoidalFunction(
                 60, 80, TrapezoidalFunction.EdgeType.Left );
             FuzzySet fsHigh = new FuzzySet( "High", function7 );
             // create a linguistic variable to represent pressure
             LinguisticVariable lvPressure = new LinguisticVariable( "Pressure", 0, 100 );
             // adding labels to the variable
             lvPressure.AddLabel( fsLow );
             lvPressure.AddLabel( fsMedium );
             lvPressure.AddLabel( fsHigh );
             
             // create a linguistic variable database
             Database db = new Database( );
             db.AddVariable( lvSteel );
             db.AddVariable( lvStove );
             db.AddVariable( lvPressure );
             
             // sample rules just to test the expression parsing
             Rule r1 = new Rule( db, "Test1", "IF Steel is not Cold and Stove is Hot then Pressure is Low" );
             Rule r2 = new Rule( db, "Test2", "IF Steel is Cold and not (Stove is Warm or Stove is Hot) then Pressure is Medium" );
             Rule r3 = new Rule( db, "Test3", "IF Steel is Cold and Stove is Warm or Stove is Hot then Pressure is High" );
             
             // testing the firing strength
             lvSteel.NumericInput = 12;
             lvStove.NumericInput = 35;
             float result = r1.EvaluateFiringStrength( );
             Console.WriteLine( result.ToString( ) );
             </code>    
             </remarks>
             
        </member>
        <member name="M:AForge.Fuzzy.Rule.#ctor(AForge.Fuzzy.Database,System.String,System.String,AForge.Fuzzy.INorm,AForge.Fuzzy.ICoNorm)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.Rule"/> class.
            </summary>
            
            <param name="fuzzyDatabase">A fuzzy <see cref="T:AForge.Fuzzy.Database"/> containig the linguistic variables
            (see <see cref="T:AForge.Fuzzy.LinguisticVariable"/>) that will be used in the Rule.</param>
            
            <param name="name">Name of this <see cref="T:AForge.Fuzzy.Rule"/>.</param>
            
            <param name="rule">A string representing the <see cref="T:AForge.Fuzzy.Rule"/>. It must be a "IF..THEN" statement.
            For a more detailed  description see <see cref="T:AForge.Fuzzy.Rule"/> class.</param>
            
            <param name="normOperator">A class that implements a <see cref="T:AForge.Fuzzy.INorm"/> interface to
            evaluate the AND operations of the Rule. </param>
            
            <param name="coNormOperator">A class that implements a <see cref="T:AForge.Fuzzy.ICoNorm"/> interface
            to evaluate the OR operations of the Rule. </param>
            
        </member>
        <member name="M:AForge.Fuzzy.Rule.#ctor(AForge.Fuzzy.Database,System.String,System.String)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.Rule"/> class using as
            CoNorm the <see cref="T:AForge.Fuzzy.MaximumCoNorm"/> and as Norm the <see cref="T:AForge.Fuzzy.MinimumNorm"/>.
            </summary>
            
            <param name="fuzzyDatabase">A fuzzy <see cref="T:AForge.Fuzzy.Database"/> containig the linguistic variables
            (see <see cref="T:AForge.Fuzzy.LinguisticVariable"/>) that will be used in the <see cref="T:AForge.Fuzzy.Rule"/>.</param>
            
            <param name="name">Name of this <see cref="T:AForge.Fuzzy.Rule"/>.</param>
            
            <param name="rule">A string representing the <see cref="T:AForge.Fuzzy.Rule"/>. It must be a "IF..THEN"
            statement. For a more detailed description see <see cref="T:AForge.Fuzzy.Rule"/> class.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.Rule.GetRPNExpression">
            <summary>
            Converts the RPN fuzzy expression into a string representation.
            </summary>
            
            <returns>String representation of the RPN fuzzy expression.</returns>
            
        </member>
        <member name="M:AForge.Fuzzy.Rule.Priority(System.String)">
            <summary>
            Defines the priority of the fuzzy operators.
            </summary>
            
            <param name="Operator">A fuzzy operator or openning parenthesis.</param>
            
            <returns>A number indicating the priority of the operator, and zero for openning
            parenthesis.</returns>
            
        </member>
        <member name="M:AForge.Fuzzy.Rule.ParseRule">
            <summary>
            Converts the Fuzzy Rule to RPN (Reverse Polish Notation). For debug proposes, the string representation of the 
            RPN expression can be acessed by calling <see cref="M:AForge.Fuzzy.Rule.GetRPNExpression"/> method.
            </summary>
            
        </member>
        <member name="M:AForge.Fuzzy.Rule.GetRuleTokens(System.String)">
            <summary>
            Performs a preprocessing on the rule, placing unary operators in proper position and breaking the string 
            space separated tokens.
            </summary>
            
            <param name="rule">Rule in string format.</param>
            
            <returns>An array of strings with tokens of the rule.</returns>
            
        </member>
        <member name="M:AForge.Fuzzy.Rule.EvaluateFiringStrength">
            <summary>
            Evaluates the firing strength of the Rule, the degree of confidence that the consequent of this Rule
            must be executed.
            </summary>
            
            <returns>The firing strength [0..1] of the Rule.</returns>
            
        </member>
        <member name="P:AForge.Fuzzy.Rule.Name">
            <summary>
            The name of the fuzzy rule.
            </summary>
            
        </member>
        <member name="P:AForge.Fuzzy.Rule.Output">
            <summary>
            The fuzzy <see cref="T:AForge.Fuzzy.Clause"/> that represents the consequent of the <see cref="T:AForge.Fuzzy.Rule"/>.
            </summary>
            
        </member>
        <member name="T:AForge.Fuzzy.FuzzySet">
            <summary>
            The class represents a fuzzy set.
            </summary>
            
            <remarks><para>The fuzzy sets are the base for all fuzzy applications. In a classical set, the membership of 
            a given value to the set can always be defined as true (1) or false (0). In fuzzy sets, this membership can be 
            a value in the range [0..1], representing the imprecision existent in many real world applications.</para>
            
            <para>Let us consider, for example, fuzzy sets representing some temperature. In a given application, there is the 
            need to represent a cool and warm temperature. Like in real life, the precise point when the temperature changes from 
            cool to warm is not easy to find, and does not makes sense. If we consider the cool around 20 degrees and warm around 
            30 degrees, it is not simple to find a break point. If we take the mean, we can consider values greater than or equal 
            25 to be warm. But we can still consider 25 a bit cool. And a bit warm at the same time. This is where fuzzy sets can 
            help.</para>
            
            <para>Fuzzy sets are often used to compose Linguistic Variables, used in Fuzzy Inference Systems.</para>
            
            <para>Sample usage:</para>
            <code>
            // creating 2 fuzzy sets to represent Cool and Warm
            TrapezoidalFunction function1 = new TrapezoidalFunction( 13, 18, 23, 28 );
            FuzzySet fsCool = new FuzzySet( "Cool", function1 );
            TrapezoidalFunction function2 = new TrapezoidalFunction( 23, 28, 33, 38 );
            FuzzySet fsWarm = new FuzzySet( "Warm", function2 );
            
            // show membership to the Cool set for some values 
            Console.WriteLine( "COOL" );
            for ( int i = 13; i &lt;= 28; i++ )
                Console.WriteLine( fsCool.GetMembership( i ) );
            
            // show membership to the Warm set for some values 
            Console.WriteLine( "WARM" );
            for ( int i = 23; i &lt;= 38; i++ )
                Console.WriteLine( fsWarm.GetMembership( i ) );
            </code>    
            </remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.FuzzySet.#ctor(System.String,AForge.Fuzzy.IMembershipFunction)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.FuzzySet"/> class.
            </summary>
            
            <param name="name">Name of the fuzzy set.</param>
            <param name="function">Membership function that will define the shape of the fuzzy set. </param>
            
        </member>
        <member name="M:AForge.Fuzzy.FuzzySet.GetMembership(System.Single)">
            <summary>
            Calculate membership of a given value to the fuzzy set.
            </summary>
            
            <param name="x">Value which membership needs to be calculated.</param>
            
            <returns>Degree of membership [0..1] of the value to the fuzzy set.</returns>
            
        </member>
        <member name="P:AForge.Fuzzy.FuzzySet.Name">
            <summary>
            Name of the fuzzy set.
            </summary>
        </member>
        <member name="P:AForge.Fuzzy.FuzzySet.LeftLimit">
            <summary>
            The leftmost x value of the fuzzy set's membership function.
            </summary>
            
        </member>
        <member name="P:AForge.Fuzzy.FuzzySet.RightLimit">
            <summary>
            The rightmost x value of the fuzzy set's membership function.
            </summary>
            
        </member>
        <member name="T:AForge.Fuzzy.FuzzyOutput">
            <summary>
            The class represents the output of a Fuzzy Inference System. 
            </summary>
            
            <remarks><para>The class keeps set of rule's output - pairs with the output fuzzy label
            and the rule's firing strength.
            </para>
            
            
            <para>Sample usage:</para>
            <code>
            // linguistic labels (fuzzy sets) that compose the distances
            FuzzySet fsNear = new FuzzySet( "Near",
                new TrapezoidalFunction( 15, 50, TrapezoidalFunction.EdgeType.Right ) );
            FuzzySet fsMedium = new FuzzySet( "Medium",
                new TrapezoidalFunction( 15, 50, 60, 100 ) );
            FuzzySet fsFar = new FuzzySet( "Far",
                new TrapezoidalFunction( 60, 100, TrapezoidalFunction.EdgeType.Left ) );
                        
            // front distance (input)
            LinguisticVariable lvFront = new LinguisticVariable( "FrontalDistance", 0, 120 );
            lvFront.AddLabel( fsNear );
            lvFront.AddLabel( fsMedium );
            lvFront.AddLabel( fsFar );
            
            // linguistic labels (fuzzy sets) that compose the angle
            FuzzySet fsZero = new FuzzySet( "Zero",
                new TrapezoidalFunction( -10, 5, 5, 10 ) );
            FuzzySet fsLP = new FuzzySet( "LittlePositive",
                new TrapezoidalFunction( 5, 10, 20, 25 ) );
            FuzzySet fsP = new FuzzySet( "Positive",
                new TrapezoidalFunction( 20, 25, 35, 40 ) );
            FuzzySet fsVP = new FuzzySet( "VeryPositive",
                new TrapezoidalFunction( 35, 40, TrapezoidalFunction.EdgeType.Left ) );
            
            // angle
            LinguisticVariable lvAngle = new LinguisticVariable( "Angle", -10, 50 );
            lvAngle.AddLabel( fsZero );
            lvAngle.AddLabel( fsLP );
            lvAngle.AddLabel( fsP );
            lvAngle.AddLabel( fsVP );
            
            // the database
            Database fuzzyDB = new Database( );
            fuzzyDB.AddVariable( lvFront );
            fuzzyDB.AddVariable( lvAngle );
            
            // creating the inference system
            InferenceSystem IS = new InferenceSystem( fuzzyDB, new CentroidDefuzzifier( 1000 ) );
            
            // going straight
            IS.NewRule( "Rule 1", "IF FrontalDistance IS Far THEN Angle IS Zero" );
            // turning left
            IS.NewRule( "Rule 2", "IF FrontalDistance IS Near THEN Angle IS Positive" );
            
            ...
            // inference section
            
            // setting inputs
            IS.SetInput( "FrontalDistance", 20 );
            
            // getting outputs
            try
            {
                FuzzyOutput fuzzyOutput = IS.ExecuteInference ( "Angle" );
            
                // showing the fuzzy output
                foreach ( FuzzyOutput.OutputConstraint oc in fuzzyOutput.OutputList )
                {
                    Console.WriteLine( oc.Label + " - " + oc.FiringStrength.ToString( ) );
                }
            }
            catch ( Exception )
            {
               ...
            }
            </code>  
            </remarks>
            
        </member>
        <member name="M:AForge.Fuzzy.FuzzyOutput.#ctor(AForge.Fuzzy.LinguisticVariable)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.FuzzyOutput"/> class.
            </summary>
            
            <param name="outputVar">A <see cref="T:AForge.Fuzzy.LinguisticVariable"/> representing a Fuzzy Inference System's output.</param>
            
        </member>
        <member name="M:AForge.Fuzzy.FuzzyOutput.AddOutput(System.String,System.Single)">
            <summary>
            Adds an output to the Fuzzy Output. 
            </summary>
            
            <param name="labelName">The name of a label representing a fuzzy rule's output.</param>
            <param name="firingStrength">The firing strength [0..1] of a fuzzy rule.</param>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The label indicated was not found in the linguistic variable.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.FuzzyOutput.ClearOutput">
            <summary>
            Removes all the linguistic variables of the database. 
            </summary>
            
        </member>
        <member name="P:AForge.Fuzzy.FuzzyOutput.OutputList">
            <summary>
            A list with <see cref="T:AForge.Fuzzy.FuzzyOutput.OutputConstraint"/> of a Fuzzy Inference System's output.
            </summary>
            
        </member>
        <member name="P:AForge.Fuzzy.FuzzyOutput.OutputVariable">
            <summary>
            Gets the <see cref="T:AForge.Fuzzy.LinguisticVariable"/> acting as a Fuzzy Inference System Output.
            </summary>
            
        </member>
        <member name="T:AForge.Fuzzy.FuzzyOutput.OutputConstraint">
            <summary>
            Inner class to store the pair fuzzy label / firing strength of 
            a fuzzy output.
            </summary>
        </member>
        <member name="M:AForge.Fuzzy.FuzzyOutput.OutputConstraint.#ctor(System.String,System.Single)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.FuzzyOutput.OutputConstraint"/> class.
            </summary>
            
            <param name="label">A string representing the output label of a <see cref="T:AForge.Fuzzy.Rule"/>.</param>
            <param name="firingStrength">The firing strength of a <see cref="T:AForge.Fuzzy.Rule"/>, to be applied to its output label.</param>
            
        </member>
        <member name="P:AForge.Fuzzy.FuzzyOutput.OutputConstraint.Label">
            <summary>
            The <see cref="T:AForge.Fuzzy.FuzzySet"/> representing the output label of a <see cref="T:AForge.Fuzzy.Rule"/>.
            </summary>
            
        </member>
        <member name="P:AForge.Fuzzy.FuzzyOutput.OutputConstraint.FiringStrength">
            <summary>
            The firing strength of a <see cref="T:AForge.Fuzzy.Rule"/>, to be applied to its output label.
            </summary>
            
        </member>
        <member name="T:AForge.Fuzzy.Clause">
             <summary>
             This class represents a fuzzy clause, a linguistic expression of the type "Variable IS Value".
             </summary>
             
             <remarks><para>A Fuzzy Clause is used to verify if a linguistic variable can be considered
             as a specific value at a specific moment. Linguistic variables can only assume value of
             their linugistic labels. Because of the nature of the Fuzzy Logic, a Variable can be 
             several of its labels at the same time, with different membership values.</para>
             
             <para>For example, a linguistic variable "temperature" can be "hot" with a membership 0.3
             and "warm" with a membership 0.7 at the same time. To obtain those memberships, Fuzzy Clauses
             "temperature is hot" and "temperature is war" can be built.</para>
             
             <para>Typically Fuzzy Clauses are used to build Fuzzy Rules (<see cref="T:AForge.Fuzzy.Rule"/>).</para>
             
             <para>Sample usage:</para>
             <code>
             // create a linguistic variable to represent temperature
             LinguisticVariable lvTemperature = new LinguisticVariable( "Temperature", 0, 80 );
            
             // create the linguistic labels (fuzzy sets) that compose the temperature 
             TrapezoidalFunction function1 = new TrapezoidalFunction( 10, 15, TrapezoidalFunction.EdgeType.Right );
             FuzzySet fsCold = new FuzzySet( "Cold", function1 );
             TrapezoidalFunction function2 = new TrapezoidalFunction( 10, 15, 20, 25 );
             FuzzySet fsCool = new FuzzySet( "Cool", function2 );
             TrapezoidalFunction function3 = new TrapezoidalFunction( 20, 25, 30, 35 );
             FuzzySet fsWarm = new FuzzySet( "Warm", function3 );
             TrapezoidalFunction function4 = new TrapezoidalFunction( 30, 35, TrapezoidalFunction.EdgeType.Left );
             FuzzySet fsHot  = new FuzzySet( "Hot" , function4 );
            
             // adding labels to the variable
             lvTemperature.AddLabel( fsCold );
             lvTemperature.AddLabel( fsCool );
             lvTemperature.AddLabel( fsWarm );
             lvTemperature.AddLabel( fsHot  );
             
             // creating the Clause
             Clause fuzzyClause = new Clause( lvTemperature, fsHot );
             // setting the numerical input of the variable to evaluate the Clause
             lvTemperature.NumericInput = 35;
             float result = fuzzyClause.Evaluate( );
             Console.WriteLine ( result.ToString( ) );
             </code>    
             </remarks>
             
        </member>
        <member name="M:AForge.Fuzzy.Clause.#ctor(AForge.Fuzzy.LinguisticVariable,AForge.Fuzzy.FuzzySet)">
            <summary>
            Initializes a new instance of the <see cref="T:AForge.Fuzzy.Clause"/> class.
            </summary>
            
            <param name="variable">Linguistic variable of the clause. </param>
            
            <param name="label">Label of the linguistic variable, a fuzzy set used as label into the linguistic variable.</param>
            
            <exception cref="T:System.Collections.Generic.KeyNotFoundException">The label indicated was not found in the linguistic variable.</exception>
            
        </member>
        <member name="M:AForge.Fuzzy.Clause.Evaluate">
            <summary>
            Evaluates the fuzzy clause.
            </summary>
            
            <returns>Degree of membership [0..1] of the clause.</returns>
            
        </member>
        <member name="M:AForge.Fuzzy.Clause.ToString">
            <summary>
            Returns the fuzzy clause in its linguistic representation.
            </summary>
            
            <returns>A string representing the fuzzy clause.</returns>
            
        </member>
        <member name="P:AForge.Fuzzy.Clause.Variable">
            <summary>
            Gets the <see cref="T:AForge.Fuzzy.LinguisticVariable"/> of the <see cref="T:AForge.Fuzzy.Clause"/>.
            </summary>
        </member>
        <member name="P:AForge.Fuzzy.Clause.Label">
            <summary>
            Gets the <see cref="T:AForge.Fuzzy.FuzzySet"/> of the <see cref="T:AForge.Fuzzy.Clause"/>.
            </summary>
        </member>
        <member name="T:AForge.Fuzzy.MinimumNorm">
            <summary>
            Minimum Norm, used to calculate the linguistic value of a AND operation. 
            </summary>
            
            <remarks><para>The minimum Norm uses a minimum operator to compute the AND
            among two fuzzy memberships. </para>
            
            <para>Sample usage:</para>
            <code>
            // creating 2 fuzzy sets to represent Cool (Temperature) and Near (Distance)
            TrapezoidalFunction function1 = new TrapezoidalFunction( 13, 18, 23, 28 );
            FuzzySet fsCool = new FuzzySet( "Cool", function1 );
            TrapezoidalFunction function2 = new TrapezoidalFunction( 23, 28, 33, 38 );
            FuzzySet fsNear = new FuzzySet( "Near", function2 );
            
            // getting memberships
            float m1 = fsCool.GetMembership( 15 );
            float m2 = fsNear.GetMembership( 35 );
            
            // computing the membership of "Cool AND Near"
            MinimumNorm AND = new MinimumNorm( );
            float result = AND.Evaluate( m1, m2 );
                         
            // show result
            Console.WriteLine( result );
            </code>
            </remarks>
            
            <seealso cref="T:AForge.Fuzzy.ProductNorm"/>
            
        </member>
        <member name="M:AForge.Fuzzy.MinimumNorm.Evaluate(System.Single,System.Single)">
            <summary>
            Calculates the numerical result of the AND operation applied to
            two fuzzy membership values using the minimum rule.
            </summary>
            
            <param name="membershipA">A fuzzy membership value, [0..1].</param>
            
            <param name="membershipB">A fuzzy membership value, [0..1].</param>
            
            <returns>The numerical result of the AND operation applied to <paramref name="membershipA"/>
            and <paramref name="membershipB"/>.</returns>
            
        </member>
    </members>
</doc>
